(PART) Shiny Apps

COVID 19 Dashboard

Here you go directly to the dashboard

Data

# Github 
covid19_confirmed_git <-"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
covid19_confirmed_git <- read_csv(url(covid19_confirmed_git))

# Worldometer
## Covid
codivid19_all <- "https://www.worldometers.info/coronavirus/"
main_table <- codivid19_all%>%
  xml2::read_html()%>%
  html_nodes(xpath='//*[@id="main_table_countries_today"]') %>%
  html_table()
main_table <- as.data.frame(main_table)

## Filtering data for Balkan countries (plus Italy and Austria)
balkan <- filter(main_table,Country.Other == "Bosnia and Herzegovina" | Country.Other == "Italy" | Country.Other == "Croatia" | Country.Other == "Serbia"  | Country.Other == "Montenegro"  | Country.Other == "Slovenia"  | Country.Other == "Austria"  | Country.Other == "North Macedonia" | Country.Other == "Greece")

# Removing comma from numbers
balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")] <- lapply(balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")], function(x) gsub(",","",x))
# Turning columns to numeric
balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")] <- lapply(balkan[c("TotalRecovered","TotalDeaths","TotalCases","NewCases","ActiveCases","Tot.Cases.1M.pop")], as.numeric)

Curve of confirmed cases

The graph shows the number of confirmed cases by the last date shown.Updates daily at around 23:59 UTC.

# Curve of confirmed cases----
columns <- colnames(covid19_confirmed_git)[5:ncol(covid19_confirmed_git)]
final <-as.data.frame(pivot_longer(covid19_confirmed_git, cols = columns, names_to = "Year", values_to = "Confirmed"))
final$Year <- as.Date.character(final$Year,"%m/%d/%y")
colnames(final) <- c("Province","Country","Lat","Long","Year","Confirmed") 
filter <- filter(final, Country == "Bosnia and Herzegovina" | Country == "Italy" | Country == "Croatia" | Country == "Serbia"  | Country == "Slovenia"  | Country == "Montenegro"  | Country == "Austria" | Country == "North Macedonia" | Country == "Greece")
p <-ggplot(filter, aes(x = Year, y = Confirmed)) + 
  geom_line(aes(color = Country), size = 1) +
  scale_color_brewer(palette="Set1")+
  theme(legend.title = element_text(size = 6),legend.text = element_text(size = 6),  
  # Remove panel background
  panel.background = element_blank(),
  # Add axis line
  axis.line = element_line(colour = "grey"),
  axis.text.x = element_text(angle = 90))+
  scale_y_log10(labels = comma)+
  scale_x_date(date_labels = "%b-%d", date_breaks = "4 week")+
  ylab("Confirmed cases")+
  labs(caption="Data source: https://github.com/CSSEGISandData/COVID-19")
ggplotly(p)

Total cases per 1 million people

```r
tot_cases_1m <- melt(balkan[,c(\Tot.Cases.1M.pop\,\Country.Other\)])
head(tot_cases_1m)
p<-ggplot(tot_cases_1m, aes(x=Country.Other,y=value,fill=Country.Other)) +
  geom_bar(stat = \identity\)+
  scale_fill_manual(name=\Country\,
                    values = c(\#E41A1C\,
                               \#377EB8\,
                               \#4DAF4A\,
                               \#984EA3\,
                               \#FF7F00\,
                               \#FFFF33\,
                               \#A65628\,
                               \#F781BF\,
                               \#999999\),
                    labels=c(\Austria\,
                             \Bosnia and Herzegovina\,
                             \Croatia\,
                             \Greece\,
                             \Italy\,
                             \Montenegro\,
                             \N.Macedonia\,
                             \Serbia\,
                             \Slovenia\))+
  labs(x=\\,y=\Total Cases per 1M people\, title = \Total Cases per 1m people - Currently\)+
  theme(legend.title = element_text(size = 8),
        axis.text.x = element_blank(),
        legend.text = element_text(size = 8),
        panel.background = element_blank(),
        axis.line = element_line(colour = \grey\))

ggplotly(p)

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## Deaths vs Recovered 

The bar chart shows the total number of recovered people in comparison to the total number of death cases. Updates daily at around 23:59 UTC.


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```r
```r
options(scipen = 9999)
p <- ggplot(balkan) +
  geom_segment( aes(x=Country.Other, xend=Country.Other, y=TotalRecovered, yend=TotalDeaths), color=\grey\) +
  geom_point( aes(x=Country.Other, y=TotalRecovered), color=rgb(0.2,0.7,0.1,0.5), size=3 ) +
  geom_point( aes(x=Country.Other, y=TotalDeaths), color=rgb(0.7,0.2,0.1,0.5), size=3 ) + coord_flip()+
  scale_y_log10()+
  theme_minimal() +
  theme(
  legend.position = \none\,
  panel.background = element_blank(),
  panel.grid = element_blank(),
  axis.line = element_line(colour = \grey\)) +
    xlab(\\) +
    ylab(\Number of cases\)+
    ggtitle(label = \Death vs Recovered\)
ggplotly(p)

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## Active cases vs New cases

The bar chart shows the number of  active cases in comparison to the number of new cases being constantly reported.


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```r
```r
# Active cases vs New cases
active_and_new <- melt(balkan[,c(\ActiveCases\,\NewCases\,\Country.Other\)])
p <- ggplot(active_and_new, aes(x=Country.Other, y=value, fill=variable)) +
  geom_bar(stat='identity', position='dodge', color=\black\ ,aes(text=paste(\Country: \,Country.Other, \\n\, variable,\:\,value, sep=\\))) +
  scale_fill_brewer(palette = \Paired\)+
  scale_y_continuous(labels=comma, trans = \log10\) +
  ylab(\Number of cases\)+
  xlab(\\)+
  theme_minimal()+
  labs(title = \\,fill=\\)+
  coord_flip()
  
ggplotly(p, tooltip = \text\)

```